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11th European Signal Processing Conference, EUSIPCO 2002

作者： Niedzwiecki, Maciej Klaput, Tomasz Kaczmarek, Piotr Faculty of Electronics Telecommunications and Computer Science Department of Automatic Control Technical University of Gdańsk ul. Narutowicza 11/12 Gdańsk Poland

The problem of estimation of a frequency of a complex sinusoidal signal buried in white measurement noise is considered. It is shown that, for a fixed model order, the variance of the autoregressive frequency estimates can be significantly reduced if the signal is decimated prior to modeling. To obtain comparable results by means of processing the signal in the original time scale, one may be forced to use autoregressive models of very high orders. Therefore the major advantage of decimation is dramatic reduction in computation. © 2002 EUSIPCO.

关键词： Frequency estimation

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YeastMed: An XML-based system for biological data integration of yeast

YeastMed: An XML-based system for biological data integratio...

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Workshop on Semantic Web Applications and Tools for Life sciences 2010, SWAT4LS 2010

作者： Briache, Abdelaali Marrakchi, Kamar Kerzazr, Amine Ismael-Navas-Delgado Aldana-Montes, José F. Hassani, Badr D. Rossi Lairini, Khalid Department of Biology Faculty of Sciences and Techniques University Abdelmalek Essaadi BP: 416 Tangier Morocco Department of Computer Languages and Computing Science Higher Technical School of Computer Science Engineering University of Malaga 29071 Malaga Spain

A key goal of bioinformatics is to create database systems and software platforms capable of storing and analysing large sets of biological data. Hundreds of biological databases are now available and provide access to huge amount of biological data. SGD, Yeastract, CYGD-MIPS, BioGrid and PhosphoGrid are five of the most visited databases by the yeast community. These sources provide complementary data on biological entities. Biologists are brought systematically to query these data sources in order to analyse the results of their experiments. Because of the heterogeneity of these sources, querying them separately and then manually combining the returned result is a complex and laborious task. To provide transparent and simultaneous access to these sources, we have developed a mediator-based system called YeastMed. In this paper, we present YeastMed focusing on its architecture.

关键词： Data integration

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Recognition of Colon Neoplasm using Contrast-Aware Capsule Network 9

Recognition of Colon Neoplasm using Contrast-Aware Capsule N...

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9th IEEE International Conference on Adaptive science and Technology, ICAST 2024

作者： Yeboah, Mavis Serwaa Kwabena Mensah, Patrick Adekoya, Adebayo Felix Abra Ayidzoe, Mighty University of Energy and Natural Resources Department of Computer Science and Informatics Sunyani Ghana Sunyani Technical University Department of Computer Science Sunyani Ghana Catholic University of Ghana Faculty of Computing Engineering and Mathematical Sciences Sunyani Ghana

ISBN: (纸本)9798350385403

Colon neoplasms are the second leading cause of mortality worldwide, and despite advances in endoscopy, even experienced endoscopists miss 22-28% of cases. Intelligent systems can bridge this gap, but current approaches face significant challenges. For instance, Convolutional Neural Networks (CNNs) excel in image recognition but require large datasets, which are often scarce in healthcare. On the other hand, Capsule Networks, suitable for smaller datasets, struggle with complex images. To address these limitations, we propose a novel contrast-aware Capsule Network for colon neoplasm recognition. By incorporating a novel contrast-aware feature transformation algorithm, our proposed model enhances extracted features and focuses on processing relevant features, achieving remarkable recognition accuracies of 96.70%, 93.68%, and 99.99% on PolyGen, ETIS-Larib, and CVC-ClinicDB datasets, respectively. Our approach outperforms state-of-the-art Capsule Network algorithms and offers a promising solution to support endoscopists in accurate colon neoplasm detection, reducing the burden of image analysis and improving patient outcomes. © 2024 IEEE.

关键词： Convolutional neural networks

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Adaptive notch filters based on combined parametric and nonparametric approach

Adaptive notch filters based on combined parametric and nonp...

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2004 IFAC Workshop on Adaptation and Learning in control and Signal Processing, ALCOSP 2004 and IFAC Workshop on Periodic control Systems, PSYCO 2004

作者： Niedzwiecki, Maciej Kaczmarek, Piotr Faculty of Electronics Telecommunications and Computer Science Department of Automatic Control Technical University of Gdańsk ul. Narutowicza 11/12 Gdańsk Poland

The problem of either elimination or extraction of an unknown number of nonstationary complex sinusoidal signals burried in noise is considered. We derive a class of adaptive notch filtering algorithms which can be efficiently (almost transientless) started, or restarted, using the results of local nonparametric, periodogram-based signal analysis. Copyright © 2004 IFAC

关键词： Frequency estimation

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RETRACTED ARTICLE: Material inner defect detection by a vibration spectrum analysis

RETRACTED ARTICLE: Material inner defect detection by a vibr...

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作者： Krejcar, Ondrej Frischer, Robert Dept. of Measurement and Control Center for Applied Cybernetics Faculty of Electrical Engineering and Computer Science Ostrava Czech Republic Dept. of Automation and Computing in Metallurgy VSB Technical University of Ostrava Ostrava Czech Republic

ISBN: (纸本)9781424474806

In this paper is described possibility of nondestructive diagnostics of solid objects by software analysis of vibration spectrum. With using platform MATLAB, we can process and evaluate information from accelerometer, which is placed on the measured object The analog signal is digitized by special I/O device dSAPCE CP-1104 and then processed offline with FFT (Fast Fourier Transform). The power spectrum is then examined by developed evaluating procedures and individual results are displayed in bar graph. When we take a look on results, there is an evidently correlation between spectrum and examining object (inner defects). © 2010 IEEE.

关键词： Spectrum analysis

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Generalized adaptive notch filters for identification of real quasi-periodically varying systems 12

Generalized adaptive notch filters for identification of rea...

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12th European Signal Processing Conference, EUSIPCO 2004

作者： Niedźwiecki, Maciej Kaczmarek, Piotr Faculty of Electronics Telecommunications and Computer Science Department of Automatic Control Technical University of Gdańsk ul. Narutowicza 11/12 Gdańsk Poland

The problem of identification/tracking of quasi-periodically varying systems is considered. This problem is a generalization, to the system case, of a classical signal processing task of either elimination or extracti... 详细信息

ISBN: (纸本)9783200001657

关键词： Adaptive filtering

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Deep Forest-Based Fall Detection in Internet of Medical Things Environment

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Computer Systems science & Engineering 2023年第6期45卷 2377-2389页

作者： Mohamed Esmail Karar Omar Reyad Hazem Ibrahim Shehata College of Computing and Information Technology Shaqra UniversityP.O.Box 33Shaqra11961Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Faculty of Computers and Artificial IntelligenceSohag UniversitySohag82524Egypt Department of Computer and Systems Engineering Faculty of EngineeringZagazig UniversityZagazig44519Egypt

This article introduces a new medical internet of things(IoT)framework for intelligent fall detection system of senior people based on our proposed deep forest *** cascade multi-layer structure of deep forest classifier allows to generate new features at each level with minimal hyperparameters compared to deep neural ***,the optimal number of the deep forest layers is automatically estimated based on the early stopping criteria of validation accuracy value at each generated *** suggested forest classifier was successfully tested and evaluated using a public SmartFall dataset,which is acquired from three-axis accelerometer in a *** includes 92781 training samples and 91025 testing samples with two labeled classes,namely non-fall and *** results of our deep forest classifier demonstrated a superior performance with the best accuracy score of 98.0%compared to three machine learning models,i.e.,K-nearest neighbors,decision trees and traditional random forest,and two deep learning models,which are dense neural networks and convolutional neural *** considering security and privacy aspects in the future work,our proposed medical IoT framework for fall detection of old people is valid for real-time healthcare application deployment.

关键词： Elderly population fall detection wireless sensor networks internet of medical things deep forest

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Combination of Fuzzy-Weighted Zero-Inconsistency and Fuzzy Decision by Opinion Score Methods in Pythagorean m -Polar Fuzzy Environment: A Case Study of Sign Language Recognition Systems

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International Journal of Information Technology and Decision Making 2023年第4期22卷 1341-1369页

作者： Albahri, O.S. Alsattar, H.A. Garfan, Salem Qahtan, Sarah Zaidan, A.A. Ahmaro, Ibraheem Y. Y. Alamoodi, A.H. Zaidan, B.B. Albahri, A.S. Al-Samarraay, Mohammed S. Jasim, Ali Najm Baqer, M.J. Department of Computing Faculty of Arts Computing and Creative Industry Universiti Pendidikan Sultan Idris Tanjung Malim 35900 Malaysia Department of Computer Center Middle Technical University's Baghdad Iraq Faculty of Engineering & It British University in Dubia Dubai United Arab Emirates Computer Science Department College of Information Technology Hebron University Hebron Palestine Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Baghdad Iraq Department of Business Administration College of Administrative Science The University of Mashreq Baghdad10021 Iraq

In the fuzzy multicriteria decision-making approach, a committee of decision-makers is usually involved in the assessment of the suitability of different alternatives based on the evaluation criteria by using linguistic terms and their equivalent fuzzy numbers. In this context, researchers have developed the Pythagorean fuzzy set (PFS) to overcome the limitation of intuitionistic fuzzy set in the description of decision-maker information such as imposing restrictions on the representation of membership and nonmembership grades. On the one hand, PFS still does not have sufficient ability and flexibility to deal with such issues. On the other hand, multipolar technology is used to operate large-scale systems in real-life situations, especially in dealing with dissatisfaction and indeterminacy grades for the alternatives of the reference set. Thus, m-polar fuzzy set is utilized and applied with other fuzzy sets because of its remarkable ability as a tool for depicting fuzziness and uncertainty under multipolar information in many circumstances. With the practical features of m-polar fuzzy set in combination with PFS, this paper employs it to extend two considerable MCDM methods, namely, fuzzy decision by opinion score method and fuzzy-weighted zero inconsistency. Such extensions, called Pythagorean m-polar fuzzy-weighted zero-inconsistency (Pm-PFWZIC) method and Pythagorean m-polar fuzzy decision by opinion score method (Pm-PFDOSM), are formulated to weight the evaluation criteria followed by alternative ranking progressively. The research methodology is presented as follows. Firstly, the mechanisms of Pm-PFWZIC and Pm-PFDOSM are formulated and integrated into the development phase. Secondly, the description of the real-world case study of the evaluation and benchmarking of the sign language recognition systems is adapted and presented. The result of Pm-PFWZIC shows that the criterion of 'finger movements' has the highest weight amongst the rest of the criteria, wherea

关键词： Decision making

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Contourlet Based Interest Points Detector

Contourlet Based Interest Points Detector

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20th IEEE International Conference on Tools with Artificial Intelligence

作者： Saydam, Samer R. El rube', Ibrahim A. Shoukry, Amin A. CS Department Computing and Information Technology College AASTMT Egypt EC Eng. Dept. College of Engineering and Technology AASTMT Egypt Computer Science and Automatic Control Department Faculty of Engineering Alexandria University Egypt

ISBN: (纸本)9780769534404

This paper proposes a robust algorithm for detecting interest points based on the NonsubSampled Contourlet Transform (NSCT). The NSCT provides multiscale decomposition with directional filters at each scale. Furthermore, NSCT is very efficient in extracting the geometric information of images and therefore it has very good feature localization. The NSCT-based point detector is compared to the widely used Harris and Difference of Gaussian (DoG) interest point detectors. The experimental results reveal the robustness of the proposed algorithm to rotation, scale and viewpoint changes.

关键词： Interest point detection corner detector local features nonsubsampled contourlet transform (NSCT)

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Improved Prediction of Metamaterial Antenna Bandwidth Using Adaptive Optimization of LSTM

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Computers, Materials & Continua 2022年第10期73卷 865-881页

作者： Doaa Sami Khafaga Amel Ali Alhussan El-Sayed M.El-kenawy Abdelhameed Ibrahim Said H.Abd Elkhalik Shady Y.El-Mashad Abdelaziz A.Abdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Systems Engineering Faculty of Engineering at ShoubraBenha UniversityEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

The design of an antenna requires a careful selection of its parameters to retain the desired ***,this task is time-consuming when the traditional approaches are employed,which represents a significant *** the other hand,machine learning presents an effective solution to this challenge through a set of regression models that can robustly assist antenna designers to find out the best set of design parameters to achieve the intended *** this paper,we propose a novel approach for accurately predicting the bandwidth of metamaterial *** proposed approach is based on employing the recently emerged guided whale optimization algorithm using adaptive particle swarm optimization to optimize the parameters of the long-short-term memory(LSTM)deep *** optimized network is used to retrieve the metamaterial bandwidth given a set of *** addition,the superiority of the proposed approach is examined in terms of a comparison with the traditional multilayer perceptron(ML),Knearest neighbors(K-NN),and the basic LSTM in terms of several evaluation criteria such as root mean square error(RMSE),mean absolute error(MAE),and mean bias error(MBE).Experimental results show that the proposed approach could achieve RMSE of(0.003018),MAE of(0.001871),and MBE of(0.000205).These values are better than those of the other competing models.

关键词： Metamaterial antenna long short term memory(LSTM) guided whale optimization algorithm(Guided WOA) adaptive dynamic particle swarm algorithm(AD-PSO)

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